Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Yasmin Elsahar, Sijung Hu, Kaddour Bouazza-Marouf, David Kerr, Annysa Mansor, Yasmin Elsahar, Sijung Hu, Kaddour Bouazza-Marouf, David Kerr, Annysa Mansor

Abstract

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain-computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user's environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

Keywords: assistive technologies; augmentative and alternative communication; machine learning; mobile health; sensing modalities; signal processing; speech disability; voice communication.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The four components of the Human Activity Assistive Technology (HAAT) model presented in [4]. The interaction between the human and the assistive technology (AT) is emphasized to highlight the relationship between the needs of the AAC users and the elements of development of high-tech solutions discussed in this review.
Figure 2
Figure 2
Components of a typical eye gaze system, adapted from [22,38]. The optical and the visual axes are used for the calibration process commonly required to set up the eye gaze system [22,39].
Figure 3
Figure 3
(a) A sample visual scanning interface activated via switch scanning. The yellow box moves vertically across the lines until a selection is made, followed by a gliding green box moving horizontally across the highlighted line until a letter is also selected. In (b), two scanning button switches are displayed.
Figure 4
Figure 4
Examples of (a) a dedicated touch-based device and (b) a non-dedicated smart device running an AAC application (APP), usually with predictive language model and speech generation capabilities.
Figure 5
Figure 5
Examples of (a) discrete breath encoding, where soft and heavy breathing blows are recorded to encode combinations of zeros and ones, or Morse codes, representing the intended messages, and (b) continuous breath encoding, where the speed, amplitude, and phase of breathing are modulated to create patterns representing the intended message.
Figure 6
Figure 6
Examples of (a) training mode, and (b) live mode of continuous breath encoding for the storage and the retrieval of breathing patterns linked to a user phrase using a mobile APP.
Figure 7
Figure 7
The components and flow diagram of a Brain–Computer Interface (BCI) system, adapted from [66,67].

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